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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Yuhuan Liu Yulong Ding Jie Jiang Bin Xiao Shuang-Hua Yang Department of Computing The Hong Kong Polytechnic University Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen Key Laboratory of Safety and Security for Next Generation of Industrial Internet College of Information Science and Engineering China University of Petroleum-Beijing Department of Computer Science University of Reading

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

The development of the Industrial Internet of Things (IIoT) is impeded by the lack of unknown protocol specifications. Protocol Reverse engineering (PRE) plays a crucial role in inferring unpublished protocol specifications by analyzing traffic messages. Since different types within a protocol often have distinct formats, inferring the protocol type is essential for subsequent reverse analysis. Natural Language Processing (NLP) models have demonstrated remarkable capabilities in various sequence tasks, and traffic messages of unknown protocols can be analyzed as sequences. In this paper, we propose a framework for clustering unknown industrial control protocol types. Our framework utilizes a transformer-based auto-encoder network to train corresponding request and response messages, leveraging intermediate layer embedding vectors learned by the network for clustering. The clustering results are employed to extract candidate keywords and establish empirical rules. Subsequently, rule-based re-clustering is performed, and its effectiveness is evaluated based on previous clustering results. Through this re-clustering process, we identify the most effective combination of keywords that define the type. We evaluate the proposed framework using three general protocols that have different type rules and successfully separate the protocol internal types completely.

关键词： Protocols Automation Industrial control Reverse engineering Transformers Natural language processing Vectors

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Semantic Heads Segmentation and Counting in Crowded Retail Environment with Convolutional Neural Networks Using Top View Depth Images

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SN computer science 2023年第1期4卷 61页

作者： Abed, Almustafa Akrout, Belhassen Amous, Ikram Department of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Alkharj 11942 Saudi Arabia Multimedia Information Systems and Advanced Computing Laboratory (MIRACL) Sfax University Sfax 3021 Tunisia ENET’COM University of Sfax National School of Electronics and Telecommunications of Sfax Road Tunis City El Ons Sfax 3018 Tunisia

In recent years, researchers have developed several techniques to accurately count the number of people in a crowded retail environment for human behavior analysis, ranging from vision and feature based approaches to machine-learning and deep learning approaches. Due to the availability and affordability of recent advanced technologies such as depth sensors and high computing powers as well as the availability of big data, the need for building accurate models to detect and count people in crowded environments arises. Deep learning approaches has proven to be highly accurate especially when there is enough data to train the model and high computation powers. People detection and counting is a challenging problem. due to several issues such as occlusion, light variations, complex backgrounds to name a few. To cope with these issues, we utilize a top-view configuration depth data to train our model. We propose a convolutional encoder decoder architecture consisting of a resnet50 encoder trained on the ImageNet dataset as a transfer learning technique and we built the decoder part of the model as a novel contribution, for segmenting and counting customers heads in a crowded retail stores where there are more than 6 individuals per square meter without compromising people privacy because the camera does not record people’s faces. The objective of our method is to segment and count people using the publicly available TV-Head Dataset and People Counting DataSet (PCDS). The results demonstrate that our model is robust and can be used for real time people counting with accurate results. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： computer vision Convolutional neural networks Intelligent retail environment People counting Top-view configuration

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Efficient Module-Lattice-based Certificateless Online/Offline Signcryption Scheme for Internet of Medical Things

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IEEE Internet of Things Journal 2025年

作者： Bai, Ye He, Debiao Yang, Zhichao Luo, Min Peng, Cong Wuhan University Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan430072 China Wuhan University School of Cyber Science and Engineering Wuhan430072 China Qilu University of Technology Shandong Academy of Sciences Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250014 China Naval University of Engineering Department of Information Security Wuhan430000 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Industrial Network and Information System Security Jinan250014 China

The Internet of Medical Things (IoMT) has achieved remote diagnosis and real-time health monitoring through intelligent sensor devices and Internet of Things (IoT) technology, providing great convenience for analyzing medical conditions between doctors and patients. However, sensitive information such as patient medical data may face security challenges such as data leakage and abuse during transmission in the IoMT. To ensure the confidentiality and unforgeability of medical data transmission, scholars have proposed many cryptographic schemes. With the development of quantum computers, schemes based on traditional cryptographic primitives have become insecure. Existing cryptographic schemes for IoMT cannot simultaneously meet the security requirements of high communication performance, low computational overhead, and resistance to quantum attacks. Therefore, we propose an efficient module-lattice-based certificateless online/offline signcryption (MLCLOOSC) scheme resistant to quantum attacks while meeting the confidentiality and unforgeability requirements under Type I and Type II attacks. Compared with five recent CLOOSC schemes, theoretical analysis, and experimental test results show that the proposed scheme outperforms the other five schemes regarding computational and communication costs and security. Therefore, our scheme is more suitable for application in IoMT scenarios. © 2014 IEEE.

关键词： Quantum computers

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Quality Assessment of Screen Content Videos

Quality Assessment of Screen Content Videos

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International Conference on Pattern Recognition and Image Analysis (IPRIA)

作者： Hossein Motamednia Pooryaa Cheraaqee Azadeh Mansouri Ahmad Mahmoudi-Aznaveh Department of Electrical and Computer Engineering Faculty of Engineering Kharazmi University Tehran Iran High Performance Computing Laboratory School of Computer Science Institute for Research in Fundamental Sciences Tehran Iran Cyber Research Institute Shahid Beheshti University Tehran Iran

Perceptual quality assessment has always been challenging due to the difficulty in modeling the no-linear human visual system. With the diversity in the contents of multimedia signals, the conventional methods for traditional media seems no longer satisfying. One of these emerging media, is the screen content images/videos (SCINs), Containing texts and computer generated graphics, SCVs cannot be sufficiently expressed with features designed for natural sceneries. Therefore, new researches tried to devise objective quality assessment metrics, specificly for screen contents. Recently, a dataset was proposed for quality assessment of screen content videos. Since screen contents are full of structures that spread in cardinal directions, we were motivated to employ the horizontal and vertical subbands of the wavelet transform to characterize these types of visual contents. The features were incorporated in a full-reference method that showed promising results on the publicly available dataset for SCV quality assessment. The method can bo accessed via: https://***/motamedNia/QASCV.

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A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems

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ACM Transactions on Evolutionary Learning and Optimization 2023年第4期3卷 1-25页

作者： Yazdani, Delaram Yazdani, Danial Yazdani, Donya Omidvar, Mohammad Nabi Gandomi, Amir H. Yao, Xin Department of Computer Engineering Mashhad Branch Azad University Mashhad*** Iran Faculty of Engineering & Information Technology University of Technology Sydney Ultimo2007 Australia AI Lab British Antarctic Survey CambridgeCB3 0ET United Kingdom School of Computing Leeds University Business School University of Leeds LeedsLS2 9JT United Kingdom Obuda University Budapest1034 Hungary Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method. © 2023 Copyright held by the owner/author(s).

关键词： Resource allocation

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Alzheimer's Disease Detection Model Based on Multimodal Data Early Fusion of Medical Neuroimaging 25

Alzheimer's Disease Detection Model Based on Multimodal Data...

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25th IEEE International Conferences on high performance computing and Communications, 9th International Conference on Data science and Systems, 21st IEEE International Conference on Smart City and 9th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Wu, Shangxiao Qin, Sheng Wang, Kaiyang Yang, Su Zhang, Shiqi School of Electronic and Information Engineering Guangxi Normal University Guangxi Key Laboratory of Brain-inspired Computing and Intelligent Chips Guilin China International Joint Audit Institute Nanjing Audit University Nanjing China Swansea University Department of Computer Science Swansea United Kingdom

ISBN: (纸本)9798350330014

Alzheimer's disease (AD) is a long-term neurodegenerative disease. The research in the field of AD is rapidly devel-oping, but most research methods are based on unimodal without considering the complementarity between different modes. In this work, a multimodal-based deep learning model is proposed for AD detection. Specifically, two medical neural image modes (i.e. magnetic resonance imaging and Positron emission computed to-mography) are used to fuse into a feature map. The convolutional block attention modules and convolutional neural networks are utilized to extract the features. A multilayer perceptron is used as a model classifier for the diagnostic classification of the learned AD features. To validate the model, experiments are conducted using the Alzheimer's Disease Neuroimaging Initiative dataset. Compared with existing AD diagnostic models, this work is better, and the accuracy of this model reaches 99.17%. It provides an optional solution for AD detection. © 2023 IEEE.

关键词： Magnetic resonance imaging

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Power Optimization for Low Transmission Delay in Software Defined Data Center Networks

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IEEE Transactions on Mobile computing 2025年第7期24卷 5716-5730页

作者： Zhao, Yong Cao, Jiannong Wang, Xingwei Li, Fuliang He, Qiang Liu, Xiaojie Department of Computing The Hong Kong Polytechnic University Hong Kong College of Computer Science and Engineering Northeastern University Shenyang China College of Medicine and Biological Information Engineering Northeastern University Shenyang China Pengcheng Laboratory Shenzhen China

Software-Defined Data Center Networks (SDDCNs) utilizes Software Defined Networking (SDN) as a network architecture to achieve highly flexible, programmable, and automated management of Data Center Networks (DCNs). The high energy consumption of DCNs remains a persistent and significant challenge. Thus, the energy saving is crucial and imperative for DCNs. Current energy-efficient solutions primarily rely on flow consolidation and dynamic device sleeping techniques to reduce energy consumption. However, these approaches often yield long Flow Completion Time (FCT), potentially resulting in violations of service-level agreements, particularly for delay-sensitive applications. In this paper, we formulate the problem of minimizing the power consumption in SDDCNs as key objective while ensuring timely FCT for delay-sensitive applications. To solve this problem, we first introduce the Active Network Generation (ANG) approach, which generates a minimal active subnet with the least number of active devices while meeting the current traffic demand. Subsequently, we propose two algorithms based on the type of applications: the Delay-Tolerant Flow Route (DTFR) algorithm for delay-tolerant applications and the Delay-Sensitive Flow Route (DSFR) algorithm for delay-sensitive applications. Simulation results demonstrate that our propose solution achieves an energy-saving rate of up to 67.77% and significantly reduces FCT compared to benchmark solutions. © 2002-2012 IEEE All rights reserved.

关键词： Energy efficiency

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Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, and Challenges

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IEEE Communications Surveys and Tutorials 2025年

作者： Satpathy, Anurag Sahoo, Manmath Narayan Swain, Chittaranjan Bellavista, Paolo Guizani, Mohsen Muhammad, Khan Bakshi, Sambit Missouri University of Science and Technology Department of Computer Science RollaMO United States National Institute of Technology Visual Surveillance Laboratory Department of Computer Science and Engineering Odisha Rourkela769008 India Atal Bihari Vajpayee Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India Department of Computer Science and Engineering Italy Machine Learning Department Abu Dhabi United Arab Emirates Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Network virtualization (NV) allows service providers (SPs) to instantiate logically isolated entities called virtual networks (VNs) on top of a substrate network (SN). Though VNs bring about multiple benefits, particularly in terms of economic costs and elasticity, they also force various technical challenges to be addressed. The primary one is the issue of optimally allocating resources to VNs, also termed virtual network embedding (VNE). This paper presents an exhaustive survey of VNE by extensively covering the state-of-the-art research field in this very active field and focusing on the emerging research trends in industry and academia over the last decade. In addition, this survey originally contributes to the literature by proposing a novel taxonomy for existing VNE solutions and providing a thorough comparative study of their strategies. © 1998-2012 IEEE.

关键词： Virtual machine

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Achieving Distributed and Privacy-Preserving Cross-Chain Transactions in Account-Model Blockchain Systems

Achieving Distributed and Privacy-Preserving Cross-Chain Tra...

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2023 IEEE International Conference on Metaverse computing, Networking and Applications, MetaCom 2023

作者： Zhang, Chuan Wang, Weijie Zhang, Weiting Nie, Jiangtian Liang, Jinwen Zhu, Liehuang Beijing Institute of Technology School of Cyberspace Science and Technology Beijing China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Guangdong China Beijing Institute of Technology School of Computer Science & Technology Beijing China Beijing Jiaotong University School of Electronic and Information Engineering Beijing China Nanyang Technological University School of Computer Science and Engineering Singapore Hong Kong Polytechnic University Department of Computing Hong Kong

ISBN: (纸本)9798350333336

Cross-Chain is an emerging technology to enable transaction circulation across different blockchain systems, breaking down transaction isolation among decentralized ecosystems. Existing cross-chain solutions are often constructed based on centralized trusted third parties, which introduce additional threats to users' identities due to potentially compromised risks on the centralized third parties. To exclude the single-point compromised risk in centralized systems, this paper focuses on a fully decentralized scenario and develops a distributed cross-chain transaction scheme, which enables cross-chain transaction exchanges while ensuring the unlinkability between payees and payers. Specifically, we first propose a cross-chain cryptocurrency exchange scheme by leveraging Ethereum's smart contracts, which achieves transaction exchanges in decentralized settings. Then, we leverage zero-knowledge succinct non-interactive argument of knowledge (i.e., zk-SNARKs) to explore a privacy-preserving method for cross-chain transactions in account-model blockchain systems, such that the unlinkability between payees and payers and the privacy of cross-chain participants can be guaranteed. Security analysis and experimental evaluations demonstrate that the proposed scheme achieves the security goal at acceptable costs. © 2023 IEEE.

关键词： Smart contract

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PIOD: An Efficient Parallel Iterative Algorithm for Solving Over-determined Equations

PIOD: An Efficient Parallel Iterative Algorithm for Solving ...

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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Min Tian Zhenguo Wei Yanlong Li Wei Du Lei Xiao Chaoshuai Xu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan Key Laboratory of High Performance Computing Jinan Institute of Supercomputing Technology

The solution of over-determined equations plays a very important role in fields such as data fitting, signal processing, and machine learning. It is of great significance in predicting natural phenomena, optimizing engineering design, and other fields. However, there is currently no efficient method to solve over-determined equations, either being not accurate enough or consuming a lot of time. In this article, we propose a parallel iterative method for solving over-determined equations, called the PIOD algorithm. By using a sub-convergence condition to terminate the iterative calculation, we have developed a task partitioning strategy for the algorithm and implemented parallelization of the solution of over-determined equations on a distributed memory system. Our proposed algorithm achieves an average speedup of 152 ×times compared to the open-source Eigen solver. Additionally, it also achieves a parallel efficiency of over 30%.

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